Modern Graph Theory Algorithms with Python: Harness the power of graph algorithms and real-world network applications using Python

Thus, solving problems that can scale with big data is perhaps more crucial today than ever. It is worth mentioning that “Modern Graph Theory Algorithms with Python” explains how to work with big data by applying the principles of network science and graph theory. You will discover how to convert various kinds of data, like spatial and time series data, into networks and then apply these networks to address practical issues.

The book “Modern Graph Theory Algorithms with Python” is dedicated to network science tools and provides you with information on how to use them in case studies with the help of Python. You will learn how to forecast the dissemination of fake news, keep track of local markets’ prices, predict stock market crashes, and contain epidemic spread. The description of each technique is accompanied by concrete examples of how it can be applied, and each chapter is filled with examples.

Later, you will discover more complex topics, such as how to build and query graph databases, how to classify data with the help of graph neural networks (GNNs), and how educational pathways work for predicting the success of students. At the end of the course, you will be ready to solve your data problems with network science and extend them with Python.

Modern Graph Theory Algorithms with Python Table of Contents:

1. Part 1: Introduction to Graphs and Networks with Examples
2. Chapter 1: What is a Network?
3. Chapter 2: Wrangling Data into Networks with NetworkX and igraph
4. Part 2: Spatial Data Applications
5. Chapter 3: Demographic Data
6. Chapter 4: Transportation Data
7. Chapter 5: Ecological Data
8. Part 3: Temporal Data Applications
9. Chapter 6: Stock Market Data
10. Chapter 7: Goods Prices/Sales Data
11. Chapter 8: Dynamic Social Networks
12. Part 4: Advanced Applications
13. Chapter 9: Machine Learning for Networks
14. Chapter 10: Pathway Mining
15. Chapter 11: Mapping Language Families — an Ontological Approach
16. Chapter 12: Graph Databases
17. Chapter 13: Putting It All Together
18. Chapter 14: New Features

Who is this course for?

1. Ideal for researchers and practitioners who work with data and are eager to adopt network science methods.
2. People are interested in network science and how graph algorithms are used to solve scientific and engineering issues.

Click on the links below to Download Modern Graph Theory Algorithms with Python!